Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID‐19 from Chest Radiographical Images: A Survey

Sinwar, Deepak; Dhaka, Vijaypal Singh; Tesfaye, Biniyam Alemu; Raghuwanshi, Ghanshyam; Kumar, Ashish; Maakar, Sunil Kr.; Agrawal, Sanjay

doi:10.1155/2022/1306664

Cited by 2 publications

(2 citation statements)

References 119 publications

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“…Many other works on COVID-19 diagnosis using AI-based approaches [ 83 , 84 , 85 ] were published by researchers. Chahar et al [ 86 ] and Sinwar et al [ 87 ] presented a survey of such learning models.…”

Section: Severity Identification Of Some Other Diseasesmentioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson’s Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer’s disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

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Section: Severity Identification Of Some Other Diseasesmentioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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“…Over the past few years, there has been a surge in studies exploring deep learning techniques to diagnose COVID-19 and pneumonia. Systematic reviews of AI-enabled COVID-19 detection can be found in [1][2][3] in 2021, [4][5][6][7][8][9][10] in 2022, and [11][12][13] in 2023. While all reviews delved into deep learning neural networks and medical image databases, there were apparent shifts in focus, from the feasibility of deep learning to this aim and limited databases in 2021, to model performance comparison and database survey in 2022, and to model enhancement/development and real-world applications recently.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

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Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID‐19 from Chest Radiographical Images: A Survey

Cited by 2 publications

References 119 publications

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

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